Overview

Dataset statistics

Number of variables19
Number of observations165632
Missing cells0
Missing cells (%)0.0%
Duplicate rows1113
Duplicate rows (%)0.7%
Total size in memory25.3 MiB
Average record size in memory160.0 B

Variable types

Categorical7
Numeric12

Alerts

Dataset has 1113 (0.7%) duplicate rowsDuplicates
z1 is highly overall correlated with x4 and 1 other fieldsHigh correlation
x2 is highly overall correlated with y2High correlation
y2 is highly overall correlated with x2High correlation
z2 is highly overall correlated with y4High correlation
y3 is highly overall correlated with y4High correlation
x4 is highly overall correlated with z1 and 1 other fieldsHigh correlation
y4 is highly overall correlated with z1 and 3 other fieldsHigh correlation
user is highly overall correlated with gender and 4 other fieldsHigh correlation
gender is highly overall correlated with user and 4 other fieldsHigh correlation
age is highly overall correlated with user and 4 other fieldsHigh correlation
how_tall_in_meters is highly overall correlated with user and 4 other fieldsHigh correlation
weight is highly overall correlated with user and 4 other fieldsHigh correlation
body_mass_index is highly overall correlated with user and 4 other fieldsHigh correlation
x1 has 7209 (4.4%) zerosZeros
x2 has 3057 (1.8%) zerosZeros

Reproduction

Analysis started2023-04-27 17:12:30.454970
Analysis finished2023-04-27 17:13:48.372114
Duration1 minute and 17.92 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

user
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
debora
51577 
wallace
51098 
katia
49797 
jose_carlos
13160 

Length

Max length11
Median length7
Mean length6.405121
Min length5

Characters and Unicode

Total characters1060893
Distinct characters15
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdebora
2nd rowdebora
3rd rowdebora
4th rowdebora
5th rowdebora

Common Values

ValueCountFrequency (%)
debora 51577
31.1%
wallace 51098
30.9%
katia 49797
30.1%
jose_carlos 13160
 
7.9%

Length

2023-04-27T20:13:48.586460image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-27T20:13:48.962483image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
debora 51577
31.1%
wallace 51098
30.9%
katia 49797
30.1%
jose_carlos 13160
 
7.9%

Most occurring characters

ValueCountFrequency (%)
a 266527
25.1%
e 115835
10.9%
l 115356
10.9%
o 77897
 
7.3%
r 64737
 
6.1%
c 64258
 
6.1%
d 51577
 
4.9%
b 51577
 
4.9%
w 51098
 
4.8%
k 49797
 
4.7%
Other values (5) 152234
14.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1047733
98.8%
Connector Punctuation 13160
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 266527
25.4%
e 115835
11.1%
l 115356
11.0%
o 77897
 
7.4%
r 64737
 
6.2%
c 64258
 
6.1%
d 51577
 
4.9%
b 51577
 
4.9%
w 51098
 
4.9%
k 49797
 
4.8%
Other values (4) 139074
13.3%
Connector Punctuation
ValueCountFrequency (%)
_ 13160
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1047733
98.8%
Common 13160
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 266527
25.4%
e 115835
11.1%
l 115356
11.0%
o 77897
 
7.4%
r 64737
 
6.2%
c 64258
 
6.1%
d 51577
 
4.9%
b 51577
 
4.9%
w 51098
 
4.9%
k 49797
 
4.8%
Other values (4) 139074
13.3%
Common
ValueCountFrequency (%)
_ 13160
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1060893
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 266527
25.1%
e 115835
10.9%
l 115356
10.9%
o 77897
 
7.3%
r 64737
 
6.1%
c 64258
 
6.1%
d 51577
 
4.9%
b 51577
 
4.9%
w 51098
 
4.8%
k 49797
 
4.7%
Other values (5) 152234
14.3%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
Woman
101374 
Man
64258 

Length

Max length5
Median length5
Mean length4.2240871
Min length3

Characters and Unicode

Total characters699644
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWoman
2nd rowWoman
3rd rowWoman
4th rowWoman
5th rowWoman

Common Values

ValueCountFrequency (%)
Woman 101374
61.2%
Man 64258
38.8%

Length

2023-04-27T20:13:49.432808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-27T20:13:49.876913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
woman 101374
61.2%
man 64258
38.8%

Most occurring characters

ValueCountFrequency (%)
a 165632
23.7%
n 165632
23.7%
W 101374
14.5%
o 101374
14.5%
m 101374
14.5%
M 64258
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 534012
76.3%
Uppercase Letter 165632
 
23.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 165632
31.0%
n 165632
31.0%
o 101374
19.0%
m 101374
19.0%
Uppercase Letter
ValueCountFrequency (%)
W 101374
61.2%
M 64258
38.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 699644
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 165632
23.7%
n 165632
23.7%
W 101374
14.5%
o 101374
14.5%
m 101374
14.5%
M 64258
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 699644
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 165632
23.7%
n 165632
23.7%
W 101374
14.5%
o 101374
14.5%
m 101374
14.5%
M 64258
 
9.2%

age
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
46
51577 
31
51098 
28
49797 
75
13160 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters331264
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46
2nd row46
3rd row46
4th row46
5th row46

Common Values

ValueCountFrequency (%)
46 51577
31.1%
31 51098
30.9%
28 49797
30.1%
75 13160
 
7.9%

Length

2023-04-27T20:13:50.159935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-27T20:13:50.530940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
46 51577
31.1%
31 51098
30.9%
28 49797
30.1%
75 13160
 
7.9%

Most occurring characters

ValueCountFrequency (%)
4 51577
15.6%
6 51577
15.6%
3 51098
15.4%
1 51098
15.4%
2 49797
15.0%
8 49797
15.0%
7 13160
 
4.0%
5 13160
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 331264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 51577
15.6%
6 51577
15.6%
3 51098
15.4%
1 51098
15.4%
2 49797
15.0%
8 49797
15.0%
7 13160
 
4.0%
5 13160
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 331264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 51577
15.6%
6 51577
15.6%
3 51098
15.4%
1 51098
15.4%
2 49797
15.0%
8 49797
15.0%
7 13160
 
4.0%
5 13160
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 331264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 51577
15.6%
6 51577
15.6%
3 51098
15.4%
1 51098
15.4%
2 49797
15.0%
8 49797
15.0%
7 13160
 
4.0%
5 13160
 
4.0%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
1.62
51577 
1.71
51098 
1.58
49797 
1.67
13160 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters662528
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.62
2nd row1.62
3rd row1.62
4th row1.62
5th row1.62

Common Values

ValueCountFrequency (%)
1.62 51577
31.1%
1.71 51098
30.9%
1.58 49797
30.1%
1.67 13160
 
7.9%

Length

2023-04-27T20:13:50.873621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-27T20:13:51.236007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1.62 51577
31.1%
1.71 51098
30.9%
1.58 49797
30.1%
1.67 13160
 
7.9%

Most occurring characters

ValueCountFrequency (%)
1 216730
32.7%
. 165632
25.0%
6 64737
 
9.8%
7 64258
 
9.7%
2 51577
 
7.8%
5 49797
 
7.5%
8 49797
 
7.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 496896
75.0%
Other Punctuation 165632
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 216730
43.6%
6 64737
 
13.0%
7 64258
 
12.9%
2 51577
 
10.4%
5 49797
 
10.0%
8 49797
 
10.0%
Other Punctuation
ValueCountFrequency (%)
. 165632
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 662528
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 216730
32.7%
. 165632
25.0%
6 64737
 
9.8%
7 64258
 
9.7%
2 51577
 
7.8%
5 49797
 
7.5%
8 49797
 
7.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 662528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 216730
32.7%
. 165632
25.0%
6 64737
 
9.8%
7 64258
 
9.7%
2 51577
 
7.8%
5 49797
 
7.5%
8 49797
 
7.5%

weight
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
75
51577 
83
51098 
55
49797 
67
13160 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters331264
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row75
2nd row75
3rd row75
4th row75
5th row75

Common Values

ValueCountFrequency (%)
75 51577
31.1%
83 51098
30.9%
55 49797
30.1%
67 13160
 
7.9%

Length

2023-04-27T20:13:51.530656image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-27T20:13:51.874225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
75 51577
31.1%
83 51098
30.9%
55 49797
30.1%
67 13160
 
7.9%

Most occurring characters

ValueCountFrequency (%)
5 151171
45.6%
7 64737
19.5%
8 51098
 
15.4%
3 51098
 
15.4%
6 13160
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 331264
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 151171
45.6%
7 64737
19.5%
8 51098
 
15.4%
3 51098
 
15.4%
6 13160
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 331264
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 151171
45.6%
7 64737
19.5%
8 51098
 
15.4%
3 51098
 
15.4%
6 13160
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 331264
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 151171
45.6%
7 64737
19.5%
8 51098
 
15.4%
3 51098
 
15.4%
6 13160
 
4.0%

body_mass_index
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
28.6
51577 
28.4
51098 
22.0
49797 
24.0
13160 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters662528
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28.6
2nd row28.6
3rd row28.6
4th row28.6
5th row28.6

Common Values

ValueCountFrequency (%)
28.6 51577
31.1%
28.4 51098
30.9%
22.0 49797
30.1%
24.0 13160
 
7.9%

Length

2023-04-27T20:13:52.158830image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-27T20:13:52.501450image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
28.6 51577
31.1%
28.4 51098
30.9%
22.0 49797
30.1%
24.0 13160
 
7.9%

Most occurring characters

ValueCountFrequency (%)
2 215429
32.5%
. 165632
25.0%
8 102675
15.5%
4 64258
 
9.7%
0 62957
 
9.5%
6 51577
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 496896
75.0%
Other Punctuation 165632
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 215429
43.4%
8 102675
20.7%
4 64258
 
12.9%
0 62957
 
12.7%
6 51577
 
10.4%
Other Punctuation
ValueCountFrequency (%)
. 165632
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 662528
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 215429
32.5%
. 165632
25.0%
8 102675
15.5%
4 64258
 
9.7%
0 62957
 
9.5%
6 51577
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 662528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 215429
32.5%
. 165632
25.0%
8 102675
15.5%
4 64258
 
9.7%
0 62957
 
9.5%
6 51577
 
7.8%

x1
Real number (ℝ)

Distinct130
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-6.649319
Minimum-306
Maximum509
Zeros7209
Zeros (%)4.4%
Negative120682
Negative (%)72.9%
Memory size2.5 MiB
2023-04-27T20:13:52.877069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-306
5-th percentile-28
Q1-12
median-6
Q30
95-th percentile10
Maximum509
Range815
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.616273
Coefficient of variation (CV)-1.7469868
Kurtosis28.078657
Mean-6.649319
Median Absolute Deviation (MAD)6
Skewness0.23261614
Sum-1101340
Variance134.93779
MonotonicityNot monotonic
2023-04-27T20:13:53.260080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1 7952
 
4.8%
-6 7280
 
4.4%
-7 7229
 
4.4%
0 7209
 
4.4%
-5 6958
 
4.2%
-2 6864
 
4.1%
-10 6758
 
4.1%
-8 6538
 
3.9%
-11 6522
 
3.9%
-4 6480
 
3.9%
Other values (120) 95842
57.9%
ValueCountFrequency (%)
-306 1
 
< 0.1%
-73 3
< 0.1%
-72 1
 
< 0.1%
-71 1
 
< 0.1%
-70 3
< 0.1%
-69 1
 
< 0.1%
-68 4
< 0.1%
-67 3
< 0.1%
-66 2
< 0.1%
-65 4
< 0.1%
ValueCountFrequency (%)
509 1
 
< 0.1%
172 1
 
< 0.1%
54 1
 
< 0.1%
52 1
 
< 0.1%
51 1
 
< 0.1%
50 1
 
< 0.1%
49 2
< 0.1%
48 4
< 0.1%
47 4
< 0.1%
46 4
< 0.1%

y1
Real number (ℝ)

Distinct190
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.293591
Minimum-271
Maximum533
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size2.5 MiB
2023-04-27T20:13:53.694757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-271
5-th percentile31
Q178
median94
Q3101
95-th percentile120
Maximum533
Range804
Interquartile range (IQR)23

Descriptive statistics

Standard deviation23.895881
Coefficient of variation (CV)0.27064117
Kurtosis2.7143806
Mean88.293591
Median Absolute Deviation (MAD)8
Skewness-0.91544319
Sum14624244
Variance571.01312
MonotonicityNot monotonic
2023-04-27T20:13:54.197394image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 7968
 
4.8%
94 7654
 
4.6%
93 7119
 
4.3%
96 7013
 
4.2%
102 6538
 
3.9%
101 6425
 
3.9%
92 6069
 
3.7%
100 5133
 
3.1%
97 5076
 
3.1%
91 4792
 
2.9%
Other values (180) 101845
61.5%
ValueCountFrequency (%)
-271 1
 
< 0.1%
1 1
 
< 0.1%
7 2
 
< 0.1%
9 1
 
< 0.1%
10 3
 
< 0.1%
11 26
 
< 0.1%
12 45
 
< 0.1%
13 81
< 0.1%
14 109
0.1%
15 155
0.1%
ValueCountFrequency (%)
533 1
< 0.1%
371 1
< 0.1%
212 1
< 0.1%
205 1
< 0.1%
196 1
< 0.1%
190 1
< 0.1%
189 1
< 0.1%
188 1
< 0.1%
187 1
< 0.1%
186 2
< 0.1%

z1
Real number (ℝ)

Distinct335
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-93.164449
Minimum-603
Maximum411
Zeros23
Zeros (%)< 0.1%
Negative165212
Negative (%)99.7%
Memory size2.5 MiB
2023-04-27T20:13:54.512403image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-603
5-th percentile-152
Q1-120
median-98
Q3-64
95-th percentile-23
Maximum411
Range1014
Interquartile range (IQR)56

Descriptive statistics

Standard deviation39.409487
Coefficient of variation (CV)-0.42300993
Kurtosis7.3460128
Mean-93.164449
Median Absolute Deviation (MAD)25
Skewness-0.36077353
Sum-15431014
Variance1553.1077
MonotonicityNot monotonic
2023-04-27T20:13:54.902333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-98 3771
 
2.3%
-99 3698
 
2.2%
-97 3473
 
2.1%
-100 3341
 
2.0%
-96 3270
 
2.0%
-95 3156
 
1.9%
-94 2807
 
1.7%
-93 2701
 
1.6%
-101 2495
 
1.5%
-92 2411
 
1.5%
Other values (325) 134509
81.2%
ValueCountFrequency (%)
-603 4
< 0.1%
-602 4
< 0.1%
-601 1
 
< 0.1%
-574 1
 
< 0.1%
-568 1
 
< 0.1%
-564 1
 
< 0.1%
-563 1
 
< 0.1%
-562 2
< 0.1%
-561 2
< 0.1%
-558 1
 
< 0.1%
ValueCountFrequency (%)
411 1
< 0.1%
184 1
< 0.1%
74 1
< 0.1%
71 1
< 0.1%
67 1
< 0.1%
64 1
< 0.1%
59 2
< 0.1%
55 2
< 0.1%
53 1
< 0.1%
52 1
< 0.1%

x2
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct688
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-87.827956
Minimum-494
Maximum473
Zeros3057
Zeros (%)1.8%
Negative106161
Negative (%)64.1%
Memory size2.5 MiB
2023-04-27T20:13:55.290906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-494
5-th percentile-492
Q1-35
median-9
Q34
95-th percentile22
Maximum473
Range967
Interquartile range (IQR)39

Descriptive statistics

Standard deviation169.43561
Coefficient of variation (CV)-1.9291762
Kurtosis0.93668377
Mean-87.827956
Median Absolute Deviation (MAD)15
Skewness-1.590708
Sum-14547120
Variance28708.424
MonotonicityNot monotonic
2023-04-27T20:13:55.670763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-492 5126
 
3.1%
-493 5045
 
3.0%
4 4459
 
2.7%
3 4362
 
2.6%
2 4125
 
2.5%
5 4091
 
2.5%
6 3636
 
2.2%
-21 3610
 
2.2%
1 3275
 
2.0%
0 3057
 
1.8%
Other values (678) 124846
75.4%
ValueCountFrequency (%)
-494 176
 
0.1%
-493 5045
3.0%
-492 5126
3.1%
-491 1909
 
1.2%
-490 858
 
0.5%
-489 486
 
0.3%
-488 377
 
0.2%
-487 319
 
0.2%
-486 263
 
0.2%
-485 246
 
0.1%
ValueCountFrequency (%)
473 2
< 0.1%
471 2
< 0.1%
469 1
 
< 0.1%
468 1
 
< 0.1%
467 1
 
< 0.1%
460 2
< 0.1%
459 2
< 0.1%
457 2
< 0.1%
455 3
< 0.1%
452 1
 
< 0.1%

y2
Real number (ℝ)

Distinct718
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-52.065911
Minimum-517
Maximum295
Zeros365
Zeros (%)0.2%
Negative61047
Negative (%)36.9%
Memory size2.5 MiB
2023-04-27T20:13:56.135873image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-517
5-th percentile-515
Q1-29
median27
Q386
95-th percentile95
Maximum295
Range812
Interquartile range (IQR)115

Descriptive statistics

Standard deviation205.16008
Coefficient of variation (CV)-3.9403916
Kurtosis0.42937307
Mean-52.065911
Median Absolute Deviation (MAD)58
Skewness-1.4196722
Sum-8623781
Variance42090.659
MonotonicityNot monotonic
2023-04-27T20:13:56.526068image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-516 6095
 
3.7%
88 5765
 
3.5%
87 5368
 
3.2%
89 5031
 
3.0%
90 4385
 
2.6%
86 4136
 
2.5%
85 3770
 
2.3%
-515 3721
 
2.2%
91 3595
 
2.2%
22 3555
 
2.1%
Other values (708) 120211
72.6%
ValueCountFrequency (%)
-517 559
 
0.3%
-516 6095
3.7%
-515 3721
2.2%
-514 1809
 
1.1%
-513 836
 
0.5%
-512 518
 
0.3%
-511 340
 
0.2%
-510 300
 
0.2%
-509 248
 
0.1%
-508 275
 
0.2%
ValueCountFrequency (%)
295 1
 
< 0.1%
270 1
 
< 0.1%
241 2
< 0.1%
227 1
 
< 0.1%
226 1
 
< 0.1%
218 1
 
< 0.1%
214 1
 
< 0.1%
208 1
 
< 0.1%
205 3
< 0.1%
204 1
 
< 0.1%

z2
Real number (ℝ)

Distinct703
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-175.05565
Minimum-617
Maximum122
Zeros31
Zeros (%)< 0.1%
Negative165029
Negative (%)99.6%
Memory size2.5 MiB
2023-04-27T20:13:56.903125image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-617
5-th percentile-615
Q1-141
median-118
Q3-29
95-th percentile-18
Maximum122
Range739
Interquartile range (IQR)112

Descriptive statistics

Standard deviation192.81711
Coefficient of variation (CV)-1.1014618
Kurtosis0.52901378
Mean-175.05565
Median Absolute Deviation (MAD)85
Skewness-1.4080864
Sum-28994817
Variance37178.438
MonotonicityNot monotonic
2023-04-27T20:13:57.283946image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-616 6072
 
3.7%
-123 4243
 
2.6%
-21 4183
 
2.5%
-25 4126
 
2.5%
-124 3991
 
2.4%
-122 3890
 
2.3%
-22 3880
 
2.3%
-615 3721
 
2.2%
-26 3716
 
2.2%
-24 3678
 
2.2%
Other values (693) 124132
74.9%
ValueCountFrequency (%)
-617 441
 
0.3%
-616 6072
3.7%
-615 3721
2.2%
-614 1837
 
1.1%
-613 951
 
0.6%
-612 548
 
0.3%
-611 366
 
0.2%
-610 293
 
0.2%
-609 273
 
0.2%
-608 261
 
0.2%
ValueCountFrequency (%)
122 1
< 0.1%
116 1
< 0.1%
113 1
< 0.1%
102 2
< 0.1%
101 1
< 0.1%
99 2
< 0.1%
96 1
< 0.1%
92 1
< 0.1%
91 1
< 0.1%
86 2
< 0.1%

x3
Real number (ℝ)

Distinct900
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.423517
Minimum-499
Maximum507
Zeros989
Zeros (%)0.6%
Negative23688
Negative (%)14.3%
Memory size2.5 MiB
2023-04-27T20:13:57.735816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-499
5-th percentile-63
Q19
median22
Q334
95-th percentile70
Maximum507
Range1006
Interquartile range (IQR)25

Descriptive statistics

Standard deviation52.635546
Coefficient of variation (CV)3.0209484
Kurtosis35.648641
Mean17.423517
Median Absolute Deviation (MAD)13
Skewness-3.6890815
Sum2885892
Variance2770.5007
MonotonicityNot monotonic
2023-04-27T20:13:58.137769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
38 4505
 
2.7%
27 4416
 
2.7%
29 4360
 
2.6%
26 4007
 
2.4%
28 3902
 
2.4%
23 3775
 
2.3%
22 3691
 
2.2%
11 3668
 
2.2%
25 3668
 
2.2%
16 3601
 
2.2%
Other values (890) 126039
76.1%
ValueCountFrequency (%)
-499 132
0.1%
-498 23
 
< 0.1%
-497 18
 
< 0.1%
-496 17
 
< 0.1%
-495 15
 
< 0.1%
-494 15
 
< 0.1%
-493 18
 
< 0.1%
-492 9
 
< 0.1%
-491 12
 
< 0.1%
-490 8
 
< 0.1%
ValueCountFrequency (%)
507 1
< 0.1%
502 1
< 0.1%
499 2
< 0.1%
496 1
< 0.1%
490 1
< 0.1%
486 1
< 0.1%
485 2
< 0.1%
484 1
< 0.1%
482 1
< 0.1%
478 1
< 0.1%

y3
Real number (ℝ)

Distinct685
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.51706
Minimum-506
Maximum517
Zeros56
Zeros (%)< 0.1%
Negative1209
Negative (%)0.7%
Memory size2.5 MiB
2023-04-27T20:13:58.634433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-506
5-th percentile54
Q195
median107
Q3120
95-th percentile172
Maximum517
Range1023
Interquartile range (IQR)25

Descriptive statistics

Standard deviation54.155987
Coefficient of variation (CV)0.51815454
Kurtosis60.579731
Mean104.51706
Median Absolute Deviation (MAD)13
Skewness-5.5480287
Sum17311369
Variance2932.8709
MonotonicityNot monotonic
2023-04-27T20:13:59.179560image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
108 13818
 
8.3%
107 10253
 
6.2%
104 5807
 
3.5%
109 5756
 
3.5%
103 4682
 
2.8%
123 4538
 
2.7%
106 3916
 
2.4%
105 3894
 
2.4%
122 3790
 
2.3%
124 3398
 
2.1%
Other values (675) 105780
63.9%
ValueCountFrequency (%)
-506 127
0.1%
-505 21
 
< 0.1%
-504 14
 
< 0.1%
-503 19
 
< 0.1%
-502 16
 
< 0.1%
-501 19
 
< 0.1%
-500 14
 
< 0.1%
-499 13
 
< 0.1%
-498 10
 
< 0.1%
-497 9
 
< 0.1%
ValueCountFrequency (%)
517 2
< 0.1%
496 1
< 0.1%
471 1
< 0.1%
447 2
< 0.1%
417 1
< 0.1%
397 1
< 0.1%
391 2
< 0.1%
390 1
< 0.1%
387 1
< 0.1%
384 1
< 0.1%

z3
Real number (ℝ)

Distinct749
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-93.881641
Minimum-613
Maximum410
Zeros25
Zeros (%)< 0.1%
Negative163569
Negative (%)98.8%
Memory size2.5 MiB
2023-04-27T20:13:59.633532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-613
5-th percentile-135
Q1-103
median-90
Q3-80
95-th percentile-55
Maximum410
Range1023
Interquartile range (IQR)23

Descriptive statistics

Standard deviation45.38977
Coefficient of variation (CV)-0.48347866
Kurtosis62.72381
Mean-93.881641
Median Absolute Deviation (MAD)12
Skewness-5.512837
Sum-15549804
Variance2060.2312
MonotonicityNot monotonic
2023-04-27T20:14:00.246165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-102 5836
 
3.5%
-88 4601
 
2.8%
-87 4376
 
2.6%
-86 4324
 
2.6%
-101 4294
 
2.6%
-89 4286
 
2.6%
-84 4210
 
2.5%
-103 4100
 
2.5%
-81 4083
 
2.5%
-85 3976
 
2.4%
Other values (739) 121546
73.4%
ValueCountFrequency (%)
-613 89
0.1%
-612 16
 
< 0.1%
-611 20
 
< 0.1%
-610 13
 
< 0.1%
-609 14
 
< 0.1%
-608 23
 
< 0.1%
-607 13
 
< 0.1%
-606 15
 
< 0.1%
-605 16
 
< 0.1%
-604 11
 
< 0.1%
ValueCountFrequency (%)
410 4
< 0.1%
409 1
 
< 0.1%
405 1
 
< 0.1%
341 1
 
< 0.1%
326 1
 
< 0.1%
298 1
 
< 0.1%
290 1
 
< 0.1%
285 1
 
< 0.1%
278 1
 
< 0.1%
277 1
 
< 0.1%

x4
Real number (ℝ)

Distinct333
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-167.64121
Minimum-702
Maximum-13
Zeros0
Zeros (%)0.0%
Negative165632
Negative (%)100.0%
Memory size2.5 MiB
2023-04-27T20:14:00.796158image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-702
5-th percentile-223
Q1-190
median-168
Q3-153
95-th percentile-93
Maximum-13
Range689
Interquartile range (IQR)37

Descriptive statistics

Standard deviation38.311336
Coefficient of variation (CV)-0.22853173
Kurtosis18.366268
Mean-167.64121
Median Absolute Deviation (MAD)18
Skewness-1.0406682
Sum-27766749
Variance1467.7584
MonotonicityNot monotonic
2023-04-27T20:14:01.178174image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-164 4020
 
2.4%
-162 3822
 
2.3%
-163 3523
 
2.1%
-161 3377
 
2.0%
-160 3193
 
1.9%
-165 2967
 
1.8%
-159 2931
 
1.8%
-158 2662
 
1.6%
-166 2391
 
1.4%
-157 2315
 
1.4%
Other values (323) 134431
81.2%
ValueCountFrequency (%)
-702 10
< 0.1%
-701 18
< 0.1%
-700 19
< 0.1%
-699 8
< 0.1%
-698 3
 
< 0.1%
-697 1
 
< 0.1%
-696 1
 
< 0.1%
-695 2
 
< 0.1%
-694 1
 
< 0.1%
-691 1
 
< 0.1%
ValueCountFrequency (%)
-13 1
< 0.1%
-16 1
< 0.1%
-33 1
< 0.1%
-34 1
< 0.1%
-38 1
< 0.1%
-42 1
< 0.1%
-46 1
< 0.1%
-47 1
< 0.1%
-48 1
< 0.1%
-50 2
< 0.1%

y4
Real number (ℝ)

Distinct204
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-92.625235
Minimum-526
Maximum86
Zeros0
Zeros (%)0.0%
Negative165630
Negative (%)> 99.9%
Memory size2.5 MiB
2023-04-27T20:14:01.584380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-526
5-th percentile-131
Q1-103
median-91
Q3-80
95-th percentile-63
Maximum86
Range612
Interquartile range (IQR)23

Descriptive statistics

Standard deviation19.968653
Coefficient of variation (CV)-0.21558545
Kurtosis2.4733298
Mean-92.625235
Median Absolute Deviation (MAD)11
Skewness-0.51010592
Sum-15341703
Variance398.74712
MonotonicityNot monotonic
2023-04-27T20:14:01.995334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-94 7001
 
4.2%
-95 6211
 
3.7%
-93 5856
 
3.5%
-90 5271
 
3.2%
-91 4998
 
3.0%
-89 4526
 
2.7%
-92 4387
 
2.6%
-88 3907
 
2.4%
-87 3835
 
2.3%
-77 3339
 
2.0%
Other values (194) 116301
70.2%
ValueCountFrequency (%)
-526 1
 
< 0.1%
-228 1
 
< 0.1%
-213 3
< 0.1%
-211 2
 
< 0.1%
-210 1
 
< 0.1%
-208 1
 
< 0.1%
-207 3
< 0.1%
-206 5
< 0.1%
-205 2
 
< 0.1%
-204 5
< 0.1%
ValueCountFrequency (%)
86 1
 
< 0.1%
9 1
 
< 0.1%
-7 2
 
< 0.1%
-9 1
 
< 0.1%
-11 1
 
< 0.1%
-13 1
 
< 0.1%
-15 2
 
< 0.1%
-16 2
 
< 0.1%
-17 6
< 0.1%
-18 9
< 0.1%

z4
Real number (ℝ)

Distinct152
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-159.65099
Minimum-537
Maximum-43
Zeros0
Zeros (%)0.0%
Negative165632
Negative (%)100.0%
Memory size2.5 MiB
2023-04-27T20:14:02.360344image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-537
5-th percentile-180
Q1-167
median-160
Q3-153
95-th percentile-136
Maximum-43
Range494
Interquartile range (IQR)14

Descriptive statistics

Standard deviation13.22102
Coefficient of variation (CV)-0.082812017
Kurtosis6.61758
Mean-159.65099
Median Absolute Deviation (MAD)7
Skewness0.63566379
Sum-26443312
Variance174.79537
MonotonicityNot monotonic
2023-04-27T20:14:02.762562image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-162 6859
 
4.1%
-158 6770
 
4.1%
-163 6762
 
4.1%
-159 6641
 
4.0%
-161 6402
 
3.9%
-160 6114
 
3.7%
-157 6110
 
3.7%
-164 5956
 
3.6%
-156 5740
 
3.5%
-165 5721
 
3.5%
Other values (142) 102557
61.9%
ValueCountFrequency (%)
-537 1
< 0.1%
-259 1
< 0.1%
-251 1
< 0.1%
-231 1
< 0.1%
-221 1
< 0.1%
-219 1
< 0.1%
-218 1
< 0.1%
-216 2
< 0.1%
-215 1
< 0.1%
-214 1
< 0.1%
ValueCountFrequency (%)
-43 1
< 0.1%
-56 1
< 0.1%
-66 1
< 0.1%
-68 2
< 0.1%
-69 1
< 0.1%
-71 1
< 0.1%
-73 1
< 0.1%
-74 1
< 0.1%
-77 1
< 0.1%
-78 2
< 0.1%

class
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
sitting
50631 
standing
47370 
walking
43390 
standingup
12414 
sittingdown
11827 

Length

Max length11
Median length7
Mean length7.7964645
Min length7

Characters and Unicode

Total characters1291344
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowsitting
2nd rowsitting
3rd rowsitting
4th rowsitting
5th rowsitting

Common Values

ValueCountFrequency (%)
sitting 50631
30.6%
standing 47370
28.6%
walking 43390
26.2%
standingup 12414
 
7.5%
sittingdown 11827
 
7.1%

Length

2023-04-27T20:14:03.108994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-27T20:14:03.552803image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
sitting 50631
30.6%
standing 47370
28.6%
walking 43390
26.2%
standingup 12414
 
7.5%
sittingdown 11827
 
7.1%

Most occurring characters

ValueCountFrequency (%)
n 237243
18.4%
i 228090
17.7%
t 184700
14.3%
g 165632
12.8%
s 122242
9.5%
a 103174
8.0%
d 71611
 
5.5%
w 55217
 
4.3%
l 43390
 
3.4%
k 43390
 
3.4%
Other values (3) 36655
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1291344
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 237243
18.4%
i 228090
17.7%
t 184700
14.3%
g 165632
12.8%
s 122242
9.5%
a 103174
8.0%
d 71611
 
5.5%
w 55217
 
4.3%
l 43390
 
3.4%
k 43390
 
3.4%
Other values (3) 36655
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 1291344
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 237243
18.4%
i 228090
17.7%
t 184700
14.3%
g 165632
12.8%
s 122242
9.5%
a 103174
8.0%
d 71611
 
5.5%
w 55217
 
4.3%
l 43390
 
3.4%
k 43390
 
3.4%
Other values (3) 36655
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1291344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 237243
18.4%
i 228090
17.7%
t 184700
14.3%
g 165632
12.8%
s 122242
9.5%
a 103174
8.0%
d 71611
 
5.5%
w 55217
 
4.3%
l 43390
 
3.4%
k 43390
 
3.4%
Other values (3) 36655
 
2.8%

Interactions

2023-04-27T20:13:40.495990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:49.711622image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:54.403896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:59.499837image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:03.505003image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:07.682820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:12.817141image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:17.676500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:21.840165image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:26.364627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:30.934360image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:35.816278image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:40.932293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:50.117655image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:54.782741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:59.871762image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:03.887259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:08.150909image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:13.252116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:18.036378image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:22.260004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:26.858257image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:31.511448image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:36.292487image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:41.313721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:50.526336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:55.156341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:00.205116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:04.230539image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:09.024436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:13.709753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:18.393343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:22.630636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:27.308117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:31.946131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:36.694247image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:41.714525image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:50.900994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:55.541993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:00.502255image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:04.545486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:09.505497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:14.042755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:18.752950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:23.059632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:27.637911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:32.268554image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:37.050313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:42.008953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:51.260005image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:55.982692image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:00.791379image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:04.838607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:09.858324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:14.492598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:19.191993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:23.389019image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:28.012503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:32.678917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:37.401503image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:42.369642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:51.701386image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:56.437518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:01.160637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:05.172648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:10.218605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:14.827030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:19.552107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:23.731897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:28.398523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:33.146200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:37.922213image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:42.740999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:52.144927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:56.958410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:01.483598image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:05.501052image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:10.591274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:15.260624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:19.901506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:24.211923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:28.820825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:33.505353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:38.301346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:43.054199image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:52.437647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:57.442236image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:01.811856image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:05.891930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:10.986478image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:15.629353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:20.203506image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:24.572032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:29.146840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:33.918287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:38.629998image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:43.360974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:52.815584image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:57.802749image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:02.121731image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:06.217933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:11.310479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:16.034346image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:20.501971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:24.881935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:29.451849image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:34.240895image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:38.970031image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:43.674628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:53.185155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:58.137016image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:02.438509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:06.537248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:11.669339image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:16.398272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:20.854156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:25.258298image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:29.801960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:34.605676image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:39.359432image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:44.043975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:53.628293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:58.635786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:02.799225image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:06.914523image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:12.085327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:16.814224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:21.243310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:25.641408image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:30.168490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:34.999467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:39.864755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:44.403971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:54.040857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:12:59.171423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:03.160672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:07.300705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:12.469374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:17.335336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:21.514429image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:26.001318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:30.547668image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:35.350140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-27T20:13:40.175428image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-04-27T20:14:03.934597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
x1y1z1x2y2z2x3y3z3x4y4z4usergenderagehow_tall_in_metersweightbody_mass_indexclass
x11.0000.1650.1100.1490.0750.3240.100-0.2020.0450.064-0.158-0.0510.0520.0300.0520.0520.0520.0520.075
y10.1651.000-0.3610.2510.320-0.175-0.2430.2890.160-0.3840.3190.2060.2600.2260.2600.2600.2600.2600.247
z10.110-0.3611.000-0.087-0.2430.3740.121-0.4520.1060.619-0.555-0.1870.2170.2940.2170.2170.2170.2170.317
x20.1490.251-0.0871.0000.7330.294-0.088-0.0240.289-0.1550.0620.2710.3360.2730.3360.3360.3360.3360.408
y20.0750.320-0.2430.7331.0000.195-0.0580.1510.257-0.2300.2120.2480.3430.2630.3430.3430.3430.3430.493
z20.324-0.1750.3740.2940.1951.000-0.130-0.4420.0100.400-0.5500.1990.3540.2200.3540.3540.3540.3540.491
x30.100-0.2430.121-0.088-0.058-0.1301.000-0.163-0.1710.1580.051-0.2540.1970.1250.1970.1970.1970.1970.206
y3-0.2020.289-0.452-0.0240.151-0.442-0.1631.0000.113-0.3470.585-0.0310.1380.1940.1380.1380.1380.1380.269
z30.0450.1600.1060.2890.2570.010-0.1710.1131.0000.0090.0690.1530.2620.1460.2620.2620.2620.2620.216
x40.064-0.3840.619-0.155-0.2300.4000.158-0.3470.0091.000-0.583-0.0730.2000.3290.2000.2000.2000.2000.339
y4-0.1580.319-0.5550.0620.212-0.5500.0510.5850.069-0.5831.000-0.0620.2200.1250.2200.2200.2200.2200.192
z4-0.0510.206-0.1870.2710.2480.199-0.254-0.0310.153-0.073-0.0621.0000.2040.2350.2040.2040.2040.2040.193
user0.0520.2600.2170.3360.3430.3540.1970.1380.2620.2000.2200.2041.0001.0001.0001.0001.0001.0000.055
gender0.0300.2260.2940.2730.2630.2200.1250.1940.1460.3290.1250.2351.0001.0001.0001.0001.0001.0000.032
age0.0520.2600.2170.3360.3430.3540.1970.1380.2620.2000.2200.2041.0001.0001.0001.0001.0001.0000.055
how_tall_in_meters0.0520.2600.2170.3360.3430.3540.1970.1380.2620.2000.2200.2041.0001.0001.0001.0001.0001.0000.055
weight0.0520.2600.2170.3360.3430.3540.1970.1380.2620.2000.2200.2041.0001.0001.0001.0001.0001.0000.055
body_mass_index0.0520.2600.2170.3360.3430.3540.1970.1380.2620.2000.2200.2041.0001.0001.0001.0001.0001.0000.055
class0.0750.2470.3170.4080.4930.4910.2060.2690.2160.3390.1920.1930.0550.0320.0550.0550.0550.0551.000

Missing values

2023-04-27T20:13:45.029935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-27T20:13:47.180433image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

usergenderagehow_tall_in_metersweightbody_mass_indexx1y1z1x2y2z2x3y3z3x4y4z4class
0deboraWoman461.627528.6-392-63-2318-195104-92-150-103-147.0sitting
1deboraWoman461.627528.6-394-64-2118-18-14104-90-149-104-145.0sitting
2deboraWoman461.627528.6-197-61-1220-15-13104-90-151-104-144.0sitting
3deboraWoman461.627528.6-296-57-1521-16-13104-89-153-103-142.0sitting
4deboraWoman461.627528.6-196-61-1320-15-13104-89-153-104-143.0sitting
5deboraWoman461.627528.6-295-62-1419-16-13104-89-153-104-142.0sitting
6deboraWoman461.627528.61100-62-1022-12-13104-90-151-104-143.0sitting
7deboraWoman461.627528.6-197-63-1320-15-12104-88-151-104-142.0sitting
8deboraWoman461.627528.6-198-63-1419-17-13104-90-152-103-144.0sitting
9deboraWoman461.627528.6098-61-1122-13-13104-90-151-104-144.0sitting
usergenderagehow_tall_in_metersweightbody_mass_indexx1y1z1x2y2z2x3y3z3x4y4z4class
165623jose_carlosMan751.676724.0-396-147681-9015120-118-200-80-166.0walking
165624jose_carlosMan751.676724.0-1094-149-1087-9512121-117-189-81-159.0walking
165625jose_carlosMan751.676724.0-894-150-889-10414119-101-193-77-153.0walking
165626jose_carlosMan751.676724.0-696-142-291-10314121-102-194-78-156.0walking
165627jose_carlosMan751.676724.0-1394-150-1388-9816120-99-194-79-157.0walking
165628jose_carlosMan751.676724.0293-148-888-10015120-100-189-77-160.0walking
165629jose_carlosMan751.676724.0-194-147-1787-9917121-99-190-78-158.0walking
165630jose_carlosMan751.676724.0-1093-143-1986-1040114-101-185-80-153.0walking
165631jose_carlosMan751.676724.0-1586-152-1988-117-44155-25-185-84-156.0walking
165632jose_carlosMan751.676724.0-1486-144-1885-107-3114-128-210-88-148.0walking

Duplicate rows

Most frequently occurring

usergenderagehow_tall_in_metersweightbody_mass_indexx1y1z1x2y2z2x3y3z3x4y4z4class# duplicates
157deboraWoman461.627528.6-576-32-122-32696-67-150-106-162.0sitting8
209deboraWoman461.627528.6-476-32-123-31394-71-153-110-165.0sitting7
664wallaceMan311.718328.4167-65-36-8-286659-103-97-119-185.0sitting6
872wallaceMan311.718328.4469-61-188-247265-99-91-118-166.0sitting6
994wallaceMan311.718328.4670-59-179-236558-105-98-124-173.0sitting6
995wallaceMan311.718328.4670-59-179-236558-105-98-124-172.0sitting6
135deboraWoman461.627528.6-575-36-322-31294-72-155-109-166.0sitting5
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